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Chemical Processes

Chemical Processes: The series of physical and chemical transformations that substances undergo to produce desired products.
This includes a wide range of industrial, laboratory, and natural processes such as synthesis, separation, purification, and analysis.
Optimizing chemical processes is crucial for improving efficiency, reducing costs, and minimizing environmental impact.
Advances in artificial intelligence and machine learning are revolutionizing chemical research by helping identify the most effective protocols and streamlining processes.
Experince the power of AI-assisted chemical research with tools like PubCompare.ai, which can easily locate the best protocols from literature, preprints, and patents through intelligent comparisons.

Most cited protocols related to «Chemical Processes»

Technical consistency (i.e. reproducibility and linearity) of the 3AA method was compared to two conventional chromatographic separations used for small molecule and amino acid analyses. Pancreatic cancer cell extracts (biological triplicates) either undiluted or diluted five-fold, were analyzed using (i) the 3AA method, as well as (ii) a 15 min gradient on a Kinetex HILIC column (150 × 2.1 mm i.d., 1.7 μm particle size – Phenomenex, Torrance, CA, USA) and (iii) a 23 min gradient on an Acquity UHPLC BEH Amide Column (2.1×100 mm, 1.7μm – Waters, Milford, MA, USA). In (ii), samples were analyzed on the Kinetex HILIC at 350 μl/min (mobile phases: A: ACN; B: 18 mΩ H2O, 20 mM (NH4)2CO3, 0.1% NH4OH; gradient: 1.5 min hold at 5% B; 5-60% B in 8.5 min; 60-95% B in 0.5 min at 0.5 ml/min; 95% hold for 2 min; 95-5% B in 0.5 min; 5 hold for 2 min; column temperature: 25°C). In (iii), samples were analyzed on the Acquity UHPLC BEH Amide Column (2.1×100 mm, 1.7μm – Waters, Milford, MA, USA) at 500 μl/min (mobile phases: A: 18 mΩ H2O, 20 mM (NH4)2CO3 pH 4.00; B: acetonitrile; gradient: 3 min hold 85% B; 85-30% B in 9 min; 30-2% B in 3 min; 2-85% of B in 1 min; 85% equilibration for 7 min; column temperature: 60°C).
The UHPLC system was coupled online with a QExactive mass spectrometer (Thermo, San Jose, CA, USA), scanning in Full MS mode (2 μscans) at 70,000 resolution from 60-900 m/z, with 4 kV spray voltage, 15 sheath gas and 5 auxiliary gas, operated in positive ion mode. Calibration was performed before each analysis using a positive calibration mix (Piercenet – Thermo Fisher, Rockford, IL, USA). Limits of detection (LOD) were characterized by determining the smallest injected amino acid amount required to provide a signal to noise (S/N) ratio greater than three using < 5 ppm error on the accurate intact mass. Based on a conservative definition for Limit of Quantitation (LOQ), these values were calculated to be three fold higher than determined LODs.
MS data acquired from the QExactive was converted from .raw file format to.mzXML format using MassMatrix (Cleveland, OH, USA). Amino acid assignments were performed using MAVEN (Princeton, NJ, USA). The MAVEN software platform provides tools for peak picking, feature detection and metabolite assignment against the KEGG pathway database. Assignments were further confirmed using a process for chemical formula determination using isotopic patterns and accurate intact mass (Clasquin et al. 2012 ). Analyte retention times were confirmed by comparison with external standard retention times, as indicated above.
Relative quantitation was performed by exporting integrated peak areas values into Excel (Microsoft, Redmond, CA, USA) for statistical analysis including T-Test and ANOVA (significance threshold for p-values < 0.05) and unsupervised Principal Component Analysis (PCA) (Pan et al. 2007 (link); Fonville et al. 2010 ), calculated through the MultiBase macro (freely available at www.NumericalDynamics.com).
Publication 2015
acetonitrile Amides Amino Acids Biopharmaceuticals Cell Extracts Chemical Processes Chromatography Isotopes neuro-oncological ventral antigen 2, human Pancreatic Carcinoma Retention (Psychology)
The GMD uses a Microsoft SQL Server 2008™ as the relational database backend for relating the mass spectrum and retention behaviour to an analyte, i.e. the chemically modified compound, which is mapped to represent a metabolite (Fig. 1) (Hummel et al. 2008 ). Both analyte and metabolite have the properties of a chemical compound and are linked to structures archived as .mol-files and InChI™ codes (http://www.iupac.org/inchi/). A typical metabolite has one to two analytes, which are generated by the chemical derivatization process inherent to the GC-MS profiling technique. Each analyte has multiple technological versions of MSTs. These replicate mass spectra and RIs are empirically determined using different mass spectral technologies, e.g. time of flight, quadrupole or ion trap based mass detectors, and variations of gas chromatographic systems (Strehmel et al. 2008 (link)).

Excerpt of the GMD scheme. MSTs (mass spectral tags, i.e. repeatedly observed mass spectra with retention behaviour) are linked to analytes via experiments and a supervised annotation process. Likewise, analytes are mapped to metabolites. Structural information has been added to both types of compounds, the metabolites and their respective analytes

In the current GMD release, 6,187 mass spectra are available representing 2,444 analytes and 1,535 metabolites. It should be noted that the GMD compendium is biased towards GC-MS accessible, stable, primary metabolites. Therefore, the structural moieties of the metabolite classes, amino acids, organic acids, fatty acids, fatty alcohols, sugars, sugar alcohols and respective conjugates dominate. Structural annotations are in most cases stereo-chemically correct, even though routine GC-MS profiling (Lisec et al. 2006 (link), Wagner et al. 2003 (link)) allows only the differentiation of anomeric, epimeric structures and E/Z-geometric isomers.
Publication 2010
Acids Amino Acids Chemical Processes chemical properties DNA Replication Fatty Acids Fatty Alcohols Gas Chromatography Gas Chromatography-Mass Spectrometry Isomerism Mass Spectrometry Retention (Psychology) Sugar Alcohols Sugars
The TOPAS-nBio extensions provide options for sub-cellular geometries, scoring, physics and chemistry. While most users will just need to adjust the values of the parameters used by the new classes, the classes can be modified with minimal coding requirements to adjust each aspect of a user’s simulation. Due to the modular nature of the extensions, users can select to only install the features they need for their simulations by downloading the necessary files and adding them to their TOPAS executable.
TOPAS-nBio facilitates and extends the use and configuration of the physical and chemical processes provided by Geant4-DNA (15 –18 ). The physical processes of Geant4-DNA, originally intended for radiation transport in liquid water, have recently included cross sections for DNA constituents (19 ), due to the availability of elastic and inelastic cross sections for these materials, which are also available in TOPAS-nBio.
The Geant4 toolkit provides users with basic 3D geometric shapes (solids), which include volumes such as boxes, ellipsoids, cylinders and spheres. All geometries in TOPAS-nBio are made up of either a single Geant4 solid, computer-aided-designed (CAD) solids or a combination of two or more of these solids. In some cases, unions of these solids are used (e.g., to create the double-helix DNA backbone from the union of spheres). The Geant4-DNA user community and others working on track-structure codes also work on new DNA or cell geometries and new DNA repair models. We actively collaborate with some of these groups to include the latest developments in our simulation framework. Several of the features presented here, e.g., interfaces to the DNAFabric code (20 –22 (link)) and DNA repair models (23 (link)–26 ), were developed as part of such collaborations.
Publication 2019
Cells Chemical Processes DNA Repair Electromagnetic Radiation Helix (Snails) Physical Examination Physical Processes Vertebral Column
The Geant4-DNA project extended the Geant4 toolkit to perform water radiolysis simulations by providing models for the physical processes of the interaction of ionizing radiation at very low energies, reported in (Bernal et al. 2015 (link); Incerti et al. 2010 (link)), and the chemical processes for the subsequent pre-chemical and nonhomogeneous chemical interactions (Mathieu Karamitros et al. 2011 ; M. Karamitros et al. 2014 (link)). The radiolysis of liquid water simulations is performed in three stages. In the first stage, called the “physical stage” (< 10−15 s), the so-called
G4EmDNAPhysics_option1 constructor is used to simulate the ionization, excitation and vibrational excitation of water molecules resulting from the interaction of the primary ionizing particles and their secondaries. In the second stage, called the “pre-chemical stage” (10−15–10−12 s), initial chemical species resulting from dissociative decay or auto-ionization of excited water molecules (H2O*) and ionized water molecules (H2O+) in addition to the thermalization of sub-excitation electrons (esub) are simulated. In the third stage, called the “chemical stage” (10−12–10−6 s), the initial chemical species diffuse and react with each other under specific rates, producing new chemical species and reducing the number of initial chemical species. Although some reactions can also occur in the pre-chemical stage (Frongillo et al. 1998 (link)), for simplicity it is assumed that these occur at the beginning of the chemical stage (Hervé du Penhoat et al. 2000 (link)) and up to 10−6 s, at which time all chemical products are considered homogeneously distributed. In Geant4-DNA, the chemical species diffuse step-by-step by Brownian motion (based on the solution to the Smoluchowsky equation in three dimensions (Risken 1989 (link))) through the medium, which is considered as a continuum. In addition, Geant4-DNA assumes that the reactions are diffusion-controlled (M. Karamitros et al. 2014 (link); Mathieu Karamitros et al. 2011 ); that is, the reaction time between two bodies is negligible in comparison with the time for the two bodies to diffuse in the same neighborhood (Rubinstein and Torquato 1988 (link)). Thus a reaction occurred every time two chemical species reached a distance smaller than their reaction radius (Plante 2011b (link)). Limitations of the approach to the chemical stage adopted by Geant4-DNA are described in (Bernal et al. 2015 (link)).
All three stages are performed history-by-history and step-by-step, independent of subsequent histories. The medium is assumed to be water of neutral pH and an ambient temperature of 25°C.
Publication 2018
Chemical Processes Diffusion Electrons Human Body Physical Examination Physical Processes Radiation, Ionizing Radius Vibration

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Publication 2008
Albumins BLOOD Chemical Processes Coagulation, Blood DNA Chips Nitrogen Oxides Peroxide, Hydrogen Piranhas Pressure Silicon Submersion Tissue, Membrane

Most recents protocols related to «Chemical Processes»

To explore the rate control steps of the physical and chemical adsorption process, the kinetic data were fitted by the pseudo-first-order and pseudo-second-order kinetic models. The solution pH was adjusted to pH 4, while the other conditions were the same as in the batch experiments, only changing the adsorption time (4–720 min). The adsorption Q (mg g−1) was calculated according to eqn (1). The pseudo-first-order and pseudo-second-order kinetic models are shown in eqn (2) and (3):
The pseudo-first-order kinetic model:
The pseudo-second-order kinetic model: where Q is the adsorption capacity of MCT (mg g−1) when adsorption reached equilibrium, Qt is the adsorption capacity of MCT (mg g−1) when adsorption reached time t, K1 is the rate constant of the first-order kinetic model, and K2 is the rate constant of the second-order kinetic model.
Publication 2023
Adsorption Chemical Processes Kinetics Physical Examination

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Publication 2023
Chemical Processes Ethanol Glycol, Ethylene Stainless Steel Teflon Ultrasonics Urea
Considering the generated waste, it was decided to use the E-factor parameter which takes into account not only waste byproducts and leftover reactants, but also spent catalysts and catalyst supports, solvent losses, and anything else that can be regarded as a waste.35 (link) The higher the E-factor of a chemical process, the greater is the waste generated, the greater its negative environmental impact, and the less sustainable it is.36 (link) The E factor was calculated as follows:
Publication 2023
Chemical Processes factor A Impacts, Environmental Solvents

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Publication 2023
Chemical Processes Cognition Cytosine Differentiations, Cell DNA Chips DNA Methylation Gene Expression Genes Genetic Heterogeneity Homo sapiens Methylation Microarray Analysis Microscopy Prefrontal Cortex Proteins RNA, Messenger
The ethylene-vinyl acetate copolymer (EVA) containing 27.5 wt% of vinyl acetate content with a melt-flow index of 5.5 g/10 min (grade UL00628) was purchased from Zhonghua Quanzhou Petrochemical Co., Ltd., Fujian, China. Dicumyl peroxide (DCP) was purchased from Sigma-Aldrich. All other ingredients are industrial grades chemicals, i.e., zinc oxide (ZnO), stearic acid (SA), talc, and azodicarbonamide (ADC) purchased from Shanghai Macklin Biochemical Co., Ltd., Shanghai, China.
In order to achieve better compatibility between the CNT and EVA, two variants of multi-walled CNT were used (supplied by Nanomatics Pte. Ltd., Singapore). Multi-walled CNT were prepared by upcycling polyolefin plastics. A mixture of low-density polyethylene, high-density polyethylene, and polypropylene was used as a feedstock. Plastics were first pyrolyzed to generate oil and non-condensable pyrolysis gas. After the separation of oil by the condensation process, the gas was used as a precursor for the synthesis of the CNT via a catalytic chemical vapor deposition process. To purify and functionalize CNT, two methods were used. (1) Oxygenated CNT (O-CNT) were prepared using chlorination above 1000 °C with a modified method from [22 (link)] and subsequent treatment with air as described in [23 (link)]. (2) Acid-purified CNT (A-CNT) were prepared by boiling CNTs in the mixture of deionized water and 70% nitric acid (4:1 volume ratio) followed by filtration and drying at 110 °C.
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Publication 2023
Acids Anabolism azodicarbonamide Catalysis Chemical Processes Chlorination dicumyl peroxide ethylenevinylacetate copolymer Filtration Nitric acid Polyethylene, High-Density Polyethylene, Low-Density polyolefin Polypropylenes Pyrolysis stearic acid Talc vinyl acetate Zinc Oxide

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More about "Chemical Processes"

Chemical processes encompass a wide range of industrial, laboratory, and natural transformations that substances undergo to produce desired products.
This includes synthesis, separation, purification, and analysis procedures.
Optimizing these chemical protocols is crucial for improving efficiency, reducing costs, and minimizing environmental impact.
Advances in artificial intelligence (AI) and machine learning (ML) are revolutionizing chemical research by helping identify the most effective protocols and streamlining processes.
PubCompare.ai is an innovative tool that leverages the power of AI-assisted chemical research.
This platform can easily locate the best protocols from literature, preprints, and patents through intelligent comparisons.
Users can discover how PubCompare.ai can streamline their chemical workflows and find the most effective solutions.
The field of chemical processes involves a diverse array of techniques and substances.
For example, nitric acid is a commonly used reagent in various chemical reactions and industrial processes.
LOCTITE EA9396 AERO epoxy paste adhesive is another important material used in engineering and manufacturing applications.
Zinc nitrate hexahydrate is a compound with applications in battery technology, water treatment, and ceramic production.
Hydrofluoric acid is a highly corrosive substance used in the semiconductor industry, glass etching, and mineral processing.
Other relevant topics include the Ultima Gold cocktail, which is a liquid scintillation counting medium used in nuclear and radiochemistry experiments.
The Nicolet iS5 is a Fourier-transform infrared (FTIR) spectrometer employed for chemical analysis and identification.
N,N-dimethylformamide is a versatile organic solvent with applications in various chemical syntheses and processes.
Au colloidal nanoparticles have unique optical and catalytic properties, finding use in nanotechnology and biomedical applications.
Quartz glass capillary tubes are essential laboratory equipment used in chromatography, electrophoresis, and other analytical techniques.
Cellic Ctec3 cellulases are enzymes utilized in the production of biofuels and other biobased products from lignocellulosic biomass.
Expeience the power of AI-assisted chemical research with tools like PubCompare.ai, which can easily locate the best protocols from liteature, preprints, and patents through intelligent comparisons.